This invention relates to technology for detecting a given object from an image.
In recent years, services that store and distribute a huge amount of images and videos have been available because of advancement of networks for broader band and storage devices for higher capacity.
For a system that extensively handles contents, search technique is a key issue. A typical search technique searches text information associated with image or video contents. In the technique that searches text information, one or more keywords are entered as a query and images or videos associated with text information including the keywords are returned as search results. There is proposed another technique that extracts information from images themselves and searches the information. As disclosed in JP 2000-123173 A and JP 2007-334402 A, similar image retrieval employs a technique that registers image features obtained by quantifying the features of the images to be registered and searched in a database to enable speedy search.
To extract image features in similar image retrieval, detecting a partial area including the object to be the target of search from an image can be critical. For example, facial search utilizing similar image retrieval detects a facial area and extracts image features from the detected facial area. In similar, similar image retrieval on vehicles detects an area including a vehicle in the image.
To detect an area including a specific object from an image, techniques have been proposed that use learning data. For example, a technique configures a classifier by arranging weak classifiers for determining local feature match in a cascade through learning by an AdaBoost algorithm, using images including an object to be detected as learning data. This technique exhibits high effectiveness in the field of detection of an area including a human face.
This technique requires the classifiers to learn by category of the object to be detected. Semantic categories are not sufficient for the categories in this case. In each category, the looks of images need to be uniform on some level. Taking an example of facial detection, the front faces and the side faces are learned by different classifiers. Further, each class of learning needs a large amount of learning data.
To address the foregoing issues, techniques have been proposed that use dictionary patterns including partial images. For example, a technique registers partial images including an object to be detected to a database as a dictionary pattern and efficiently extracts a partial area similar to the dictionary pattern from an image to detect a partial area. This technique can detect objects looked differently together.